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Record W7020290439

Jalostuksen ja maantieteen vaikutus koirarotujen erilaistumisessa geneettisiksi osapopulaatioiksi

2019· dissertation· fi· W7020290439 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUEF eRepo (University of Eastern Finland) · 2019
Typedissertation
Languagefi
FieldAgricultural and Biological Sciences
TopicEntomological Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsShetlandPurebredGenetic diversitySelection (genetic algorithm)Genetic structureGenetic variationInbreedingGenetic heterogeneityGenetic variability
DOInot available

Abstract

fetched live from OpenAlex

Purebred dogs can be fragmented into within-breed subpopulations based on their geographical location and divergent selection regimes.This stratification has significance for breeding, when the subpopulations are also genetically differentiated from each other.Problems of inbreeding, such as specific genetic disorders, can accumulate within the genetic subpopulations.Therefore, identifying the genetic subpopulations and their causes can help to conserve genetic diversity and health of a dog breed.In this study, six dog breeds were analysed for the occurrence of genetic differentiation to subpopulations.This differentiation was measured by performing a genome-wide survey of 1319 single nucleotide polymorphism (SNP) markers from 142 Belgian Shepherd, 104 English Greyhound, 224 Finnish Lapphund, 90 Italian Greyhound, 608 Labrador Retriever and 95 Shetland Sheepdog.In order to find a selection-related explanation to the genetic differentiation, I compared the observed subpopulations to the individual's data about their geographical origin or breeding line.I discovered that all the breeds studied are fragmented into genetic subpopulations.The geographical origin explained the findings of the Italian Greyhound and the Shetland Sheepdog.The selection for performance explained the genetic subpopulations in the English Greyhound and in the Labrador Retriever.The morphological selection was a plausible explanation in the Belgian Shepherd and in the Finnish Lapphund.Additionally, evidence of the overlapping explanations to the genetic subpopulation differentiation was found in the Belgian Shepherd, the English Greyhound, the Italian Greyhound and the Labrador Retriever.It is probable that all the breeds studied would benefit if the findings of the genetic subpopulations were considered in their breeding program.Evidence of the genetic subpopulations within the dog breeds was convincing and therefore similar surveys for other dog breeds are recommendable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.200
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it